class_docstr: Knowledge distillation loops for training and evaluation. This module is independent of
  the model definition. This class inherits from the Pytorch Lightning's LightningModule:
  https://lightning.ai/docs/pytorch/stable/common/lightning_module.html
code: def _compute_hard_label_loss(
      self,
      output: dict,
      label: torch.Tensor,
  ):
      loss = 0
      for per_output in output.values():
          weight = per_output[WEIGHT] if WEIGHT in per_output else 1
          loss += (
              self.hard_label_loss_func(
                  input=per_output[LOGITS].squeeze(dim=1),
                  target=label,
              )
              * weight
          )
  
      return loss
  
imports: import logging
  from typing import Callable, List, Optional, Union
  import lightning.pytorch
  import torch
  import torch.nn.functional
  import torchmetrics
  from lightning.pytorch.utilities import grad_norm
  from omegaconf import DictConfig
  from torch import nn
  from torch.nn.modules.loss import _Loss
  from torchmetrics.aggregation import BaseAggregator
  from ..constants import FEATURES, LOGITS, WEIGHT
  from ..models.utils import run_model
  from .utils import apply_layerwise_lr_decay, apply_single_lr, apply_two_stages_lr, get_lr_scheduler, get_optimizer
label_desc: Use image node_id 2 for calling the DistillerLitModule obj's underlying member method code with example usage: obj._compute_hard_label_loss(output, label) and returns: loss